Capasso, Marco, Alessi, Lucia, Barigozzi, Matteo and Fagiolo, Giorgio (2009) On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: the case of unknown parameters. Advances in Complex Systems, 12 (2). pp. 157-167. ISSN 0219-5259
Full text not available from this repository.Abstract
This paper discusses some problems possibly arising when approximating via Monte-Carlo simulations the distributions of goodness-of-fit test statistics based on the empirical distribution function. We argue that failing to re-estimate unknown parameters on each simulated Monte-Carlo sample — and thus avoiding to employ this information to build the test statistic — may lead to wrong, overly-conservative. Furthermore, we present some simple examples suggesting that the impact of this possible mistake may turn out to be dramatic and does not vanish as the sample size increases.
| Item Type: | Article |
|---|---|
| Official URL: | http://www.worldscinet.com/acs/ |
| Additional Information: | © 2009 World Scientific Publishing |
| Divisions: | Statistics |
| Subjects: | H Social Sciences > HA Statistics |
| Date Deposited: | 07 Jan 2011 10:45 |
| Last Modified: | 29 Oct 2024 21:12 |
| URI: | http://eprints.lse.ac.uk/id/eprint/31119 |
Actions (login required)
 |
View Item |
